{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "\n",
    "import scipy.io as sio  # for loading matlab data\n",
    "\n",
    "import numpy as np      # for numerical libs\n",
    "\n",
    "from matplotlib.ticker import FuncFormatter # for custom bar plot labels\n",
    "\n",
    "import matplotlib.pyplot as plt  # for plotting\n",
    "\n",
    "import matplotlib.pylab as pylab\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (15, 5),\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "pylab.rcParams.update(params)\n",
    "from statsmodels.tsa import stattools\n",
    "from statsmodels.graphics import gofplots\n",
    "from scipy import stats\n",
    "from statsmodels.tsa import arima_model\n",
    "from statsmodels.regression import linear_model\n",
    "from os import path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part 1: Estimate the coefficient of a linear price impact function and deduce the risk-aversion of the market maker from it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop through files to get all data from Nov3 - Nov26\n",
    "# Define function to return parameters: a) midprices and order flow b) Heights of LOB\n",
    "\n",
    "def getPF(ticker, start, end):\n",
    "    # presumption: must have the 'Nov-2014' data folder in the working directory\n",
    "    ''' startTime = 10*60*30 #10:00 (30mins after open)\n",
    "    endTime = 10*60*60*6 #15:30 (30mins before close)'''\n",
    "    \n",
    "    P = np.array([])\n",
    "    volMO = np.array([])\n",
    "    Pa3s = np.array([])\n",
    "    Pb3s = np.array([])\n",
    "    askvolSum = np.array([])\n",
    "    bidvolSum = np.array([])\n",
    "    for day in range(start,end+1):\n",
    "        dateStr = f\"{day:02}\"\n",
    "        file = 'Nov-2014/' + ticker +'_201411' + dateStr + '.mat'\n",
    "        if path.exists(file):\n",
    "            data = sio.loadmat(file)\n",
    "            LOB=data['LOB']\n",
    "            ask = np.array(LOB['SellPrice'][0][0]*1e-4)\n",
    "            askvol = np.array(LOB['SellVolume'][0][0]*1.0)\n",
    "            bid = np.array(LOB['BuyPrice'][0][0]*1e-4)\n",
    "            bidvol = np.array(LOB['BuyVolume'][0][0]*1.0)\n",
    "            vMO = np.array(LOB['VolumeMO'][0][0]*1.0)\n",
    "\n",
    "            Pa3s = np.append(Pa3s, ask[:,2])\n",
    "            Pb3s = np.append(Pb3s, bid[:,2])\n",
    "            midprice = 0.5*(bid[:,0]+ask[:,0])\n",
    "            P = np.append(P, midprice)\n",
    "            volMO = np.append(volMO, vMO[:,0]-vMO[:,1])\n",
    "            askvolSum = np.append(askvolSum, np.sum(askvol[:,0:3],axis=1))\n",
    "            bidvolSum = np.append(bidvolSum, np.sum(bidvol[:,0:3],axis=1))\n",
    "\n",
    "    OF = np.cumsum(volMO)\n",
    "    Ha = askvolSum / (Pa3s - P)\n",
    "    Hb = bidvolSum / (P - Pb3s)\n",
    "    return P, OF, Ha, Hb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "impact(slope): 8.516535910053103e-07\n",
      "intercept: -5.1032892281204695e-06\n",
      "0.0\n",
      "impact(LOB): 1.599593907717814e-06\n"
     ]
    }
   ],
   "source": [
    "# get MSFT parameters\n",
    "Pms, OF, Ha, Hb = getPF('MSFT',3,26)\n",
    "\n",
    "# a) Estimate price impact coefficient using the midprice and orderflow\n",
    "# get values at the start of every second\n",
    "P2 = Pms[::10]\n",
    "OF2 = OF[::10]\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(np.diff(OF2), np.diff(P2))\n",
    "MSFTa = slope\n",
    "print('impact(slope):', slope)\n",
    "print('intercept:', intercept)\n",
    "print(p_value)\n",
    "\n",
    "# b) estimate price impact coefficient via LOB\n",
    "lambdas = (1/Ha + 1/Hb)/2\n",
    "MSFTb = np.mean(lambdas)\n",
    "print('impact(LOB):', MSFTb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "impact(slope): 0.00011410857525993758\n",
      "intercept: -5.809990990859403e-05\n",
      "0.0\n",
      "impact(LOB): 0.0005758615962403105\n"
     ]
    }
   ],
   "source": [
    "# get GOOG parameters\n",
    "Pgg, OF, Ha, Hb = getPF('GOOG',3,26)\n",
    "\n",
    "# a) Estimate price impact coefficient using the midprice and orderflow\n",
    "# get values at the start of every second\n",
    "P2 = Pgg[::10]\n",
    "OF2 = OF[::10]\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(np.diff(OF2), np.diff(P2))\n",
    "GOOGa = slope\n",
    "print('impact(slope):', slope)\n",
    "print('intercept:', intercept)\n",
    "print(p_value)\n",
    "\n",
    "# b) estimate price impact coefficient via LOB\n",
    "lambdas = (1/Ha + 1/Hb)/2\n",
    "GOOGb = np.mean(lambdas)\n",
    "print('impact(LOB):', GOOGb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSFT Var: 0.002497142336780609\n",
      "GOOG Var: 0.3577849575469325\n",
      "\n",
      "Risk Aversion\n",
      "MSFT:\n",
      "a) 0.00017052564014097965\n",
      "b) 0.0003202848880813215\n",
      "GOOG:\n",
      "a) 0.00015946530569968046\n",
      "b) 0.0008047593730443122\n"
     ]
    }
   ],
   "source": [
    "# c) calculate the risk-aversion of the market maker\n",
    "\n",
    "# approximate fundamental vals from midprice variance\n",
    "h = 10*60*5 #number of timestamps in 5 mins\n",
    "VARms = np.var(np.diff(Pms[::h]))\n",
    "VARgg = np.var(np.diff(Pgg[::h]))\n",
    "print('MSFT Var:', VARms)\n",
    "print('GOOG Var:', VARgg)\n",
    "print()\n",
    "\n",
    "# Risk Aversion\n",
    "RA_MSFT_a = MSFTa/(2*VARms)\n",
    "RA_MSFT_b = MSFTb/(2*VARms)\n",
    "RA_GOOG_a = GOOGa/(2*VARgg)\n",
    "RA_GOOG_b = GOOGb/(2*VARgg)\n",
    "\n",
    "print('Risk Aversion')\n",
    "print('MSFT:')\n",
    "print('a)', RA_MSFT_a)\n",
    "print('b)', RA_MSFT_b)\n",
    "print('GOOG:')\n",
    "print('a)', RA_GOOG_a)\n",
    "print('b)', RA_GOOG_b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part 2: Implement trading strategy based on predicting prices via order flow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data = sio.loadmat('Nov-2014/MSFT_20141103.mat')\n",
    "LOB = data['LOB']\n",
    "\n",
    "t = (np.array((LOB['EventTime'][0][0][:,0]))-3600000*9.5)*1e-3 #time in seconds, measured from NASDAQ opening at 9:30am\n",
    "bid = np.array(LOB['BuyPrice'][0][0]*1e-4)\n",
    "bidvol = np.array(LOB['BuyVolume'][0][0]*1.0)\n",
    "ask = np.array(LOB['SellPrice'][0][0]*1e-4)\n",
    "askvol = np.array(LOB['SellVolume'][0][0]*1.0)\n",
    "nMO = np.array(LOB['NumberMO'][0][0]*1.0)\n",
    "vMO = np.array(LOB['VolumeMO'][0][0]*1.0)\n",
    "MO=np.array(LOB['MO'][0][0]*1.0)\n",
    "dt = t[1]-t[0]\n",
    "OF = np.cumsum(vMO[:,0]-vMO[:,1])\n",
    "midprice = 0.5*(bid[:,0]+ask[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define hyper parameters and function to extract parameters from running window\n",
    "\n",
    "q = 100\n",
    "estWin = 10*60*10 #10 mins\n",
    "K = 10*60*3 #3 mins (timestamps of prediction interval)\n",
    "tP = 0.02999\n",
    "lP = 0.03999\n",
    "p_upper = 0.05\n",
    "rho_lower = 0.02\n",
    "R_lower = 0.7\n",
    "\n",
    "def getParams(startT):\n",
    "    t_win = list(t[startT-estWin:startT]-t[startT-estWin])[::10]\n",
    "    OF_win = list(OF[startT-estWin:startT]-OF[startT-estWin])[::10]\n",
    "    P_win = list(midprice[startT-estWin:startT])[::10]\n",
    "    #print(len(t_win), len(OF_win), len(P_win))\n",
    "    slope, intercept, r_val, p_val, std_err = stats.linregress(t_win,OF_win)\n",
    "    a = intercept\n",
    "    b = slope\n",
    "    OF_centered = [(OF_win[i] - t_win[i]*b - a) for i in range(len(OF_win))]\n",
    "    phi, sigma = linear_model.yule_walker(OF_centered,order=1)\n",
    "    rho = 1 - phi\n",
    "    \n",
    "    OF_diff = np.diff(OF_win)\n",
    "    P_diff = np.diff(P_win)\n",
    "    #print(OF_diff.shape, P_diff.shape)\n",
    "    slope, intercept, R, p2, std_err = stats.linregress(OF_diff, P_diff)\n",
    "    impact = slope \n",
    "    P_diff_cen = [(P_diff[i] - OF_diff[i]*impact - intercept) for i in range(len(P_diff))]\n",
    "    my_DF = stattools.adfuller(P_diff_cen,1,'c',None)\n",
    "    p1 = my_DF[1]\n",
    "\n",
    "    return p1, p2, R, impact, a, b, rho, OF_centered[-1], sigma\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time: 30000\n",
      "time: 40000\n",
      "time: 50000\n",
      "time: 60000\n",
      "time: 70000\n",
      "time: 80000\n",
      "time: 90000\n",
      "time: 100000\n",
      "time: 110000\n",
      "time: 120000\n",
      "time: 130000\n",
      "time: 140000\n",
      "time: 150000\n",
      "time: 160000\n",
      "time: 170000\n",
      "time: 180000\n",
      "time: 190000\n",
      "time: 200000\n",
      "time: 210000\n"
     ]
    }
   ],
   "source": [
    "# Loop through every point in time t, carrying out our strategy\n",
    "\n",
    "# set parameters\n",
    "shares = [0]\n",
    "PnL = [0]\n",
    "startTime = 10*60*40 #10:10 (40mins after open)\n",
    "endTime = 10*60*60*6 #15:30 (30mins before close)\n",
    "maxHoldingTime = 0\n",
    "targetPrice = 0\n",
    "lossPrice = 0\n",
    "F_upper = 0\n",
    "a, b, t0 = 0, 0, 0\n",
    "\n",
    "for time in range(startTime, endTime):\n",
    "    shares.append(shares[-1])\n",
    "    PnL.append(PnL[-1])\n",
    "    if time%10000 == 0:\n",
    "        print('time:', time)\n",
    "    if shares[-1] == 0:\n",
    "        p1, p2, R, impact, a, b, rho, Fm, sigma = getParams(time)\n",
    "\n",
    "        # if conditions are met, open a position\n",
    "        if p1<p_upper and p2<p_upper and rho>rho_lower and R>R_lower:\n",
    "            # predicted change in midprice\n",
    "            dP = impact*b*K/10 + impact*Fm*((1-rho)**(K/10)-1)-impact*sigma\n",
    "            F_lower = 1.3 * sigma\n",
    "            F_upper = 2 * sigma\n",
    "            t0 = time\n",
    "            maxHoldingTime = time + K\n",
    "            if dP >= tP and Fm > -F_lower:\n",
    "                targetPrice = ask[time,0] + tP\n",
    "                lossPrice = ask[time,0] - lP\n",
    "                shares[-1] += q \n",
    "                PnL[-1] -= q*bid[time,0]\n",
    "            elif dP <= tP and Fm < F_lower:\n",
    "                targetPrice = bid[time,0] - tP\n",
    "                lossPrice = bid[time,0] + lP\n",
    "                shares[-1] -= q \n",
    "                PnL[-1] += q*ask[time,0]\n",
    "    else:\n",
    "        if shares[-1]>0: # close long position if conditions are met\n",
    "            if ask[time,0] >= targetPrice:\n",
    "                shares[-1] -= q\n",
    "                PnL[-1] += q*(ask[time,0] - 0.01)\n",
    "            elif bid[time,0] <= lossPrice:\n",
    "                shares[-1] -= q\n",
    "                PnL[-1] += q*lossPrice\n",
    "            elif time > maxHoldingTime:\n",
    "                shares[-1] -= q\n",
    "                PnL[-1] += q*bid[time,0]\n",
    "            elif OF[time] - a - b*(time-t0+estWin)/10 < -F_upper:\n",
    "                shares[-1] -= q\n",
    "                PnL[-1] += q*bid[time,0]\n",
    "\n",
    "        if shares[-1]<0: # close Short position if conditions are met\n",
    "            if bid[time,0] <= targetPrice:\n",
    "                shares[-1] += q\n",
    "                PnL[-1] -= q*(bid[time,0] + 0.01)\n",
    "            elif ask[time,0] >= lossPrice:\n",
    "                shares[-1] += q\n",
    "                PnL[-1] -= q*lossPrice\n",
    "            elif time > maxHoldingTime:\n",
    "                shares[-1] += q\n",
    "                PnL[-1] -= q*ask[time,0]\n",
    "            elif OF[time] - a - b*(time-t0+estWin)/10 > F_upper:\n",
    "                shares[-1] += q\n",
    "                PnL[-1] -= q*ask[time,0]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save PnL process\n",
    "np.savetxt('Abs_PnL.csv', np.array(PnL), delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'USD')"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the PnL process\n",
    "plt.plot(PnL)\n",
    "plt.title('Abs PnL')\n",
    "plt.xlabel('timestamp')\n",
    "plt.ylabel('USD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gross Profit: 6.001000000002023\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zoom in \n",
    "plt.plot(PnL)\n",
    "plt.title('Abs PnL')\n",
    "plt.xlabel('timestamp')\n",
    "plt.ylabel('USD')\n",
    "plt.ylim(-5,12)\n",
    "\n",
    "# Final Profit\n",
    "print('Gross Profit:', PnL[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'timestamp')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing midprice for reference\n",
    "plt.plot(midprice[startTime:endTime])\n",
    "plt.title('Midprice')\n",
    "plt.ylabel('USD')\n",
    "plt.xlabel('timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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